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The effect of chain architecture on the swelling and thermal response of thin films obtained from an amphiphilic three-arm star-shaped thermo-responsive block copolymer poly(methoxy diethylene glycol acrylate)-block-polystyrene ((PMDEGA-b-PS)(3)) is investigated by in situ neutron reflectivity (NR) measurements. The PMDEGA and PS blocks are micro-phase separated with randomly distributed PS nanodomains. The (PMDEGA-b-PS)(3) films show a transition temperature (TT) at 33 degrees C in white light interferometry. The swelling capability of the (PMDEGA-b-PS)(3) films in a D2O vapor atmosphere is better than that of films from linear PS-b-PMDEGA-b-PS triblock copolymers, which can be attributed to the hydrophilic end groups and limited size of the PS blocks in (PMDEGA-b-PS)(3). However, the swelling kinetics of the as-prepared (PMDEGA-b-PS)(3) films and the response of the swollen film to a temperature change above the TT are significantly slower than that in the PS-b-PMDEGA-b-PS films, which may be related to the conformation restriction by the star-shape. Unlike in the PS-b-PMDEGA-b-PS films, the amount of residual D2O in the collapsed (PMDEGA-b-PS)(3) films depends on the final temperature. It decreases from (9.7 +/- 0.3)% to (7.0 +/- 0.3)% or (6.0 +/- 0.3)% when the final temperatures are set to 35 degrees C, 45 degrees C and 50 degrees C, respectively. This temperature-dependent reduction of embedded D2O originates from the hindrance of chain conformation from the star-shaped chain architecture.
Most image restoration methods in astronomy rely upon probabilistic tools that infer the best solution for a deconvolution problem. They achieve good performances when the point spread function (PSF) is spatially invariant in the image plane.
However, this condition is not always satisfied in real optical systems. We propose a new method for the restoration of images affected by static and anisotropic aberrations using Deep Neural Networks that can be directly applied to sky images.
The network is trained using simulated sky images corresponding to the T80-S Telescope optical model, a 80-cm survey imager at Cerro Tololo (Chile), which are synthesized using a Zernike polynomial representation of the optical system.
Once trained, the network can be used directly on sky images, outputting a corrected version of the image that has a constant and known PSF across its field of view. The method is to be tested on the T80-S Telescope.
We present the method and results on synthetic data.
Arctic lowlands are characterized by large numbers of small waterbodies, which are known to affect surface energy budgets and the global carbon cycle. Statistical analysis of their size distributions has been hindered by the shortage of observations at sufficiently high spatial resolutions. This situation has now changed with the high-resolution (<5 m) circum-Arctic Permafrost Region Pond and Lake (PeRL) database recently becoming available. We have used this database to make the first consistent, high-resolution estimation of Arctic waterbody size distributions, with surface areas ranging from 0.0001 km(2) (100 m(2)) to 1 km(2). We found that the size distributions varied greatly across the thirty study regions investigated and that there was no single universal size distribution function (including power-law distribution functions) appropriate across all of the study regions. We did, however, find close relationships between the statistical moments (mean, variance, and skewness) of the waterbody size distributions from different study regions. Specifically, we found that the spatial variance increased linearly with mean waterbody size (R-2 = 0.97, p < 2.2e-16) and that the skewness decreased approximately hyperbolically. We have demonstrated that these relationships (1) hold across the 30 Arctic study regions covering a variety of (bio)climatic and permafrost zones, (2) hold over time in two of these study regions for which multi-decadal satellite imagery is available, and (3) can be reproduced by simulating rising water levels in a high-resolution digital elevation model. The consistent spatial and temporal relationships between the statistical moments of the waterbody size distributions underscore the dominance of topographic controls in lowland permafrost areas. These results provide motivation for further analyses of the factors involved in waterbody development and spatial distribution and for investigations into the possibility of using statistical moments to predict future hydrologic dynamics in the Arctic.
Convolutional neural networks (CNNs) have been used for a wide range of applications in astronomy, including for the restoration of degraded images using a spatially invariant point spread function (PSF) across the field of view. Most existing development techniques use a single PSF in the deconvolution process, which is unrealistic when spatially variable PSFs are present in real observation conditions. Such conditions are simulated in this work to yield more realistic data samples. We propose a method that uses a simulated spatially variable PSF for the T80-South (T80-S) telescope, an 80-cm survey imager at Cerro Tololo (Chile). The synthetic data use real parameters from the detector noise and atmospheric seeing to recreate the T80-S observational conditions for the CNN training. The method is tested on real astronomical data from the T80-S telescope. We present the simulation and training methods, the results from real T80-S image CNN prediction, and a comparison with space observatory Gaia. A CNN can fix optical aberrations, which include image distortion, PSF size and profile, and the field position variation while preserving the source's flux. The proposed restoration approach can be applied to other optical systems and to post-process adaptive optics static residual aberrations in large-diameter telescopes.